This paper presents a new algorithm for fast approximate surface reconstruction from sampled points. This algorithm works over three steps. Firstly, a raw rectangular surface is obtained, and then this surface is triangulated and smoothed in the second step. The surface vertices are fitted to the nearest input points at the end. The algorithm is very fast, numerically stable, easy to implement, and it constructs a watertight surface.In the experimental section, the algorithm is compared with other available algorithms (algorithm from CGAL library, Power Crust, Tight cocone, and Poisson reconstruction) in regards to the spent CPU time. Finally, an error of the obtained approximate surface is empirically estimated.
COBISS.SI-ID: 16262678
This paper considers a new method for the automatic generation of digital terrain models from LiDAR data. The method iterates a thin plate spline interpolated surface towards the ground, while points' residuals from the surface are inspected at each iteration, with a gradually decreasing window size. Top-hat transformation is used to enhance discontinuities caused by surface objects. Finally, parameter-free ground point filtering is achieved by automatic thresholding based on standard deviation. The experiments show that this method correctly determines DTM even in those cases of more difficult terrain features. The expected accuracy of ground point determination on those datasets commonly used in practice today is over 96%, while the average total error produced on the ISPRS benchmark dataset is under 6%.
COBISS.SI-ID: 15485718
A new method is introduced for the lossy compression of a LAS file. LAS files store the results from LiDAR scanning, and contain a huge amount of points with associated scalar values. The proposed method consists of four steps: eliminating those points within the oversampled regions, moving the position of the remaining points within the user-specified limits, variable-length coding, and arithmetic compression. This method was compared with the only known lossy LAS files compression method, as owned by LizardTechâ„¢. As shown by experimentation, the proposed method is considerably better than the referenced method.
COBISS.SI-ID: 16325654